sample_2 = {'path': 'data/S3DIS/Area_1_conferenceRoom_1.txt',
           'point_prompts': [[0.01049672, 0.47400134, 0.51851852], [0.79906279, 0.88886409, 0.23477715], [0.62417994, 0.79825932, 0.01349655],
                             [0.15126523, 0.88886409, 0.18047709], [0.54020619, 0.52041955, 0.24670433],],
           'box_prompts': [[0.03, 0.63, 0.98, 0.18, 0.78, 1.0], [0.0, 0.4, 0.0, 0.15, 0.55, 0.27], [0.2, 0.95, 0.25, 0.7, 1.0, 0.67],
                           [0.2, 0.2, 0.7, 0.25, 0.8, 0.78], [0.68, 0.85, 0., 1.0, 1.0, 0.25],[0, 0.82, 0.02, 0.2, 1, 0.38]],
}


sample_3 = {'path': 'data/S3DIS/Area_2_WC_1.txt',
           'point_prompts': [[0.31414868, 0.59265659, 0.50951199], [0.6628697,  0.90842333, 0.34036394],[0.63868905, 0.36414687, 0.94954508],
                             [0.11171063, 0.85788337, 0.18072787], 
                             [0.88589129, 0.59049676, 0.44830438],],
           'box_prompts': [[0.35, 0.8, 0.05, 0.45, 1.0, 0.4], [0.48, 0.65, 0.0, 0.55, 0.99, 0.99], [0.57, 0.2, 0.85, 0.7, 0.48, 1.0],
                           [0.61, 0., 0.33, 0.71, 0.13, 0.51],], 
}


sample_4 = {'path': 'data/S3DIS/Area_4_lobby_2.txt',
           'point_prompts': [[0.19949431, 0.28597082, 0.25131625], 
                             [0.72566372, 0.3617284,  0.65601966], [0.50316056, 0.57519641, 0.32186732],
                             [0.46396966, 0.52345679, 0.54756055],],
           'box_prompts': [[0.42, 0.45, 0.3, 0.49, 0.54, 0.65], [0.45, 0.57, 0.27, 0.55, 0.63, 0.36], [0.17, 0.35, 0., 0.25, 0.4, 0.3],
                           [0.15, 0.25, 0.4, 0.19, 0.33, 0.62], [0.17, 0.78, 0.27, 0.2, 0.84, 0.43]],
}

sample_1 = {'path': 'data/S3DIS/Area_5_office_3.txt',
           'point_prompts': [
                             [0.90161319, 0.51668286, 0.21546617], [0.98404538, 0.29024943, 0.51013408],
                             [0.76369438, 0.32458698, 0.23542251]],
           'box_prompts': [[0., 0.48, 0.23, 0.12, 0.61, 0.31], [0.4, 0.25, 0., 0.6, 0.6, 0.3], [0.45, 0.85, 0.45, 0.65, 0.99, 0.55],
                           [0.38, 0.95, 0.25, 0.48, 1.00, 0.42], [0.65, 0.45, 0., 0.75, 0.6, 0.3]],  
}

sample_0 = {'path': 'data/S3DIS/Area_6_office_9.txt',
           'point_prompts': [[0.16548, 0.27853667, 0.1886402], [0.46150787, 0.09795895, 0.26989673], [0.2904479, 0.5073498,  0.28115318],
                             [0.9304859, 0.40291342, 0.32013769], [0.802557, 0.5818576, 0.19074],
                             [0.52659518, 0.5240772, 0.40165232], [0.29337714, 0.8905976, 0.2722375], [0.563984, 0.925, 0.3803788],],
           'box_prompts': [[0.1, 0.2, 0.0, 0.2, 0.3, 0.4], [0.1, 0.02, 0.2, 0.9, 0.2, 0.3], [0.7, 0.5, 0., 0.9, 0.7, 0.4],
                           [0.85, 0.3, 0.02, 0.98, 0.5, 0.8], [0.4, 0.4, 0.3, 0.6, 0.6, 0.5], ],
}


S3DIS_samples = [sample_2, sample_3, sample_4, sample_1, sample_0]


sample_1 = {'path': 'data/ScanNet/scene0005_01.pth',
           'point_prompts': [[0.50845712, 0.4027696,  0.19570725], [0.26778319, 0.9830749,  0.44313431]], 
           'box_prompts': [[0.6, 0.6, 0., 0.83, 0.9, 0.33], [0.0, 0.57, 0.05, 0.15, 0.67, 0.48],
                           [0.48, 0.95, 0.58, 0.8, 0.99, 0.9]],
}
sample_2 = {'path': 'data/ScanNet/scene0010_01.pth',
           'point_prompts': [[0.86644632, 0.26297486, 0.5173167]], 
           'box_prompts': [[0.6, 0.72, 0.0, 0.75, 0.85, 0.6], [0.75, 0.70, 0.5, 0.92, 0.92, 0.75], [0.05, 0.92, 0.05, 0.27, 1.0, 0.82],
                           [0.35, 0.03, 0.15, 0.5, 0.1, 0.42], ],
}


sample_3 = {'path': 'data/ScanNet/scene0016_02.pth',
           'point_prompts': [[0.2898192, 0.5845358, 0.7862434], [0.8251329,0.1763976,0.2942619]],
           'box_prompts': [[0.72, 0.36, 0.1, 0.9, 0.75, 0.75], [0.27, 0.54, 0.7, 0.3, 0.65, 0.9],], 
}


sample_4 = {'path': 'data/ScanNet/scene0019_01.pth',
           'point_prompts': [[0.52182293, 0.69650459, 0.36580974], [0.6603151,  0.26341686, 0.33537653],[0.03188787, 0.65648252, 0.43863711]], 
           'box_prompts': [[0.55, 0.22, 0.05, 0.72, 0.3, 0.58], [0.0, 0.27, 0.05, 0.2, 0.35, 0.45]], }

sample_5 = {'path': 'data/ScanNet/scene0000_00.pth',
           'point_prompts': [[0.37658614, 0.11185088, 0.25310564], [0.40517676, 0.7643317,  0.16952564], [0.42705029, 0.8192997,  0.17624393]],
           'box_prompts': [],
}
sample_6 = {'path': 'data/ScanNet/scene0002_00.pth',
           'point_prompts': [[0.56711978, 0.74271345, 0.1753805 ], [0.61877084, 0.47617316, 0.23380645]],
           'box_prompts': [],
}

ScanNet_samples = [sample_1, sample_2, sample_3, sample_4, sample_5, sample_6] 


sample_0 = {'path': 'data/Objaverse/plant.npy',
           'point_prompts': [[0.50455284, 0.47794762, 0.0007253083], [0.28331658, 0.19435011, 0.77393067]], 
           'voxel_size': [0.038, 0.04],
           'box_prompts': [[0.08, 0.18, -0.02, 0.68, 0.73, 0.315]], 
           'voxel_size_box': [0.04, 0.05], 
}


sample_1 = {'path': 'data/Objaverse/human.npy',
           'point_prompts': [[0.57825595, 0.5005686,  0.11494722], [0.7136412,  0.49501216, 0.5020814 ], [0.7136412,  0.49501216, 0.5020814 ]],
           'voxel_size': [0.055, 0.045, 0.05],
           'box_prompts': [[0., 0.17, -0.01, 0.72, 0.80, 0.3], [-0.01, 0., 0.28, 0.8, 1, 0.82], [-0.01, 0.28, 0.89, 1, 0.72, 1.02]],
           'voxel_size_box': [0.055, 0.045, 0.055],
}
sample_2 = {'path': 'data/Objaverse/lock.npy',
           'point_prompts': [[0.6513301, 0.6753892, 0.52316076], [0.21359734, 0.6097132 , 0.7939796 ], [0.44947368, 0.21654338, 0.58450174]], 
           'voxel_size': [0.04, 0.05, 0.05], 
           'box_prompts': [[0.61, 0.4, 0.35, 0.8, 0.8, 0.6], [0.42, -0.02, -0.02, 1.02, 0.4, 1]],  
           'voxel_size_box': [0.04, 0.011], 
}

sample_3 = {'path': 'data/Objaverse/elephant.npy',
           'point_prompts': [[0.4394578, 0.8342078, 0.835564]],
           'voxel_size': [0.04],
           'box_prompts': [[0.25,0,0,0.8,0.35,0.23]],
           'voxel_size_box': [0.04],
}

sample_4 = {'path': 'data/Objaverse/knife_rest.npy',
           'point_prompts': [[0.3342131, 0.5378736, 0.8621972], [0.7043406, 0.4798344, 0.2585481]],
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0.21, 0.26, 0.83, 0.37, 0.9, 1], [0, 0, 0, 1, 1, 0.28]],
           'voxel_size_box': [0.04, 0.04],
}

sample_5 = {'path': 'data/Objaverse/skateboard.npy',
           'point_prompts': [[0.5026503, 0.4316724, 0.5640968], [0.2835252, 0.4883442, 0.2073544]],
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0, 0, 0.54, 1, 1, 1], [0.21, 0.75, 0, 0.34, 1, 0.5]],
           'voxel_size_box': [0.04, 0.04],
}

sample_6 = {'path': 'data/Objaverse/popcorn_machine.npy',
           'point_prompts': [[0.278306, 0.4913014, 0.7318756], [0.5867118, 0.1180351, 0.5844101]], 
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0.208, 0.157, 0.493, 0.779, 0.89, 0.925]],
           'voxel_size_box': [0.04],
}

sample_7 = {'path': 'data/Objaverse/stove.npy',
           'point_prompts': [[0.08, 0.72, 0.669], [0.9416, 0.3464, 0.3476], [0.021837, 0.281256, 0.8934]],
           'voxel_size': [0.04, 0.04, 0.04],
           'box_prompts': [[0,0,0.579,0.18,1,0.67], [0.528, 0.64, 0.508, 0.844, 0.866, 0.56]],
           'voxel_size_box': [0.04, 0.04],
}


sample_8 = {'path': 'data/Objaverse/bus_shelter.npy',
           'point_prompts': [[0.6665938, 0.5713098, 0.2139242], [0.577489, 0.915092, 0.4498839]],
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0.32, 0.36, 0, 0.924, 0.861, 0.394], [0, 0, 0.71, 1, 1, 1]],
           'voxel_size_box': [0.04, 0.04],
}

sample_9 = {'path': 'data/Objaverse/thor_hammer.npy',
           'point_prompts': [[0.6211515, 0.5109989, 0.3867725], [0.44443, 0.2363458, 0.7229376]],
           'voxel_size': [0.05, 0.05, 0.05],
           'box_prompts': [[0,0,0.723,1,1,1]],
           'voxel_size_box': [0.05, 0.05],
}

sample_10 = {'path': 'data/Objaverse/horse.npy',
           'point_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]], 
           'voxel_size_box': [0.04, 0.04],
}

sample_11 = {'path': 'data/Objaverse/dinner_booth.npy',
           'point_prompts': [
    [0.9192697, 0.4469184, 0.0017635],
    [0.4987888, 0.6916906, 0.5106028]],
           'voxel_size': [0.04, 0.04],
           'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]], 
           'voxel_size_box': [0.04, 0.04],
}

Objaverse_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6, sample_7, sample_8, sample_9, sample_10, sample_11]


sample_0 = {'path': 'data/KITTI/scene1.npy',
           'point_prompts': [[0.5527776, 0.7294311, 0.685305 ]],
           'voxel_size': [0.02],
           'box_prompts': [[0.52, 0.73, 0.56, 0.57, 0.76, 0.75]],
           'voxel_size_box': [0.01],
}


sample_1 = {'path': 'data/KITTI/scene2.npy',
           'point_prompts': [[0.5090489, 0.45589063, 0.49851784]],
           'voxel_size': [0.015],
           'box_prompts': [[0.48, 0.43, 0.34, 0.54, 0.48, 0.71]],
           'voxel_size_box': [0.015],
}


sample_2 = {'path': 'data/KITTI/scene3.npy',
           'point_prompts': [[0.5442487, 0.5907391, 0.5992437]],
           'voxel_size': [0.01],
           'box_prompts': [[0.532, 0.58, 0.37, 0.555, 0.61, 0.68]],
           'voxel_size_box': [0.01],
}

sample_3 = {'path': 'kitti/scene4.npy',
           'point_prompts': [[0.4739189, 0.4791307, 0.8351399]],
           'voxel_size': [0.01],
           'box_prompts': [[0.51, 0.2, 0.75, 0.53, 0.22, 0.9]],
           'voxel_size_box': [0.01],
}

sample_4 = {'path': 'kitti/scene5.npy',
           'point_prompts': [[0.5438917, 0.7608865, 0.5123742], [0.5131016, 0.7495122, 0.5516282]],
           'voxel_size': [0.01, 0.01],
           'box_prompts': [[0.43, 0.746, 0.39, 0.471,0.77, 0.62]],
           'voxel_size_box': [0.01],
}

sample_5 = {'path': 'kitti/scene6.npy',
           'point_prompts': [[0.4619498, 0.3496694, 0.7484359], [0.4963415, 0.5221788, 0.7358279]],
           'voxel_size': [0.008, 0.01],
           'box_prompts': [[0.5459, 0.4, 0.62, 0.559, 0.5, 0.77], [0.61,0.343,0.625,0.664,0.377,0.8261]],
           'voxel_size_box': [0.01, 0.01],
}
KITTI_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5]




sample_0 = {'path': 'data/Semantic3D/scene1.npy',
           'point_prompts': [[0.08373796, 0.61115538, 0.6007256], [0.2660193, 0.823606, 0.242315]],
           'voxel_size': [0.017, 0.017],
           'box_prompts': [[-0.02, 0.52, -0.02, 0.1, 0.7, 0.92]],
           'voxel_size_box': [0.017],
}


sample_1 = {'path': 'data/Semantic3D/scene2.npy',
           'point_prompts': [[0.79984724, 0.25791535, 0.18132911]],
           'voxel_size': [0.012],
           'box_prompts': [[0.78, 0, -0.02, 1, 0.5, 0.2]],
           'voxel_size_box': [0.012],
}



sample_2 = {'path': 'data/Semantic3D/patch19.npy',
           'point_prompts': [[0.51970197, 0.38389998, 0.33622117],
                             [0.84013408, 0.80095002, 0.24210576]],
           'voxel_size': [0.017, 0.017, 0.017, 0.017],
           'box_prompts': [],
           'voxel_size_box': [],
}

sample_3 = {'path': 'data/Semantic3D/patch0.npy',
           'point_prompts': [[0.91819174, 0.34150001, 0.25513778], [0., 0.34900001, 0.32881831]],
           'voxel_size': [0.015, 0.017, 0.017, 0.017, 0.017, 0.017, 0.017],
           'box_prompts': [],
           'voxel_size_box': [],
}

sample_4 = {'path': 'data/Semantic3D/patch1.npy',
           'point_prompts': [[0.51603703, 0.51312565, 0.50598845]],
           'voxel_size': [0.017, 0.017, 0.017, 0.017],
           'box_prompts': [],
           'voxel_size_box': [],
}

sample_5 = {'path': 'data/Semantic3D/patch50.npy',
           'point_prompts': [[0.22901525, 0.49448244, 0.52076028]],
           'voxel_size': [0.017, 0.017, 0.017, 0.017],
           'box_prompts': [[0.09, 0.44, 0.08, 0.4, 0.75, 0.98]],
           'voxel_size_box': [0.017, 0.017],
}


sample_6 = {'path': 'data/Semantic3D/patch62.npy',
           'point_prompts': [],
           'voxel_size': [],
           'box_prompts': [[0.26, 0.38, 0.24, 0.55, 0.78, 0.99]],
           'voxel_size_box': [0.017],
}

Semantic3D_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6]


VOXEL = {"point": "voxel_size", "box": "voxel_size_box", "mask": "voxel_size_mask"}

